Symposia and Conferences
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Item Database Management System Deployment on Docker Containerization for Distributed Systems(Faculty of Computing and Technology (FCT), University of Kelaniya, Sri Lanka, 2021) Kithulwatta, W.M.C.J.T.; Jayasena, K.P.N.; Kumara, B.T.G.S.; Rathnayaka, R.M.K.T.Containerization is a novel technology that brings an alternative for virtualization. Due to the most infrastructure-based features, most computer system administration engineers use Docker as the infrastructure level platform. On the Docker containers, any such kind of software service can be deployed. This study aims to evaluate Docker container based relational database management system container behavior. Currently, most scholarly research articles are existing for the database engine performance evaluation under different metrics and measurements of the database management systems. Therefore, without repeating them: this study evaluated the data storage mechanisms, security approaches, container resource usages and container features on the launching mechanism. According to the observed features and factors on the containerized database management systems, containerized database management systems are presenting more value-added features. Hence containerized database management system Docker containers can be recommended for the distributed computer systems for getting the benefit of effectiveness and efficiency.Item An Unsupervised Machine Learning Approach for Churn Prediction(4th International Conference on Advances in Computing and Technology (ICACT ‒ 2019), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2019) Prasanth, S.; Rathnayaka, R.M.K.T.; Arumawadu, H.Customer churn is one of the critical issues faced by the firms nowadays. Telecommunication industry is no exception to this rule. In this industry, keeping the existing subscriber (customer) is more valuable than acquiring a new subscriber (attracting new customers costs approximately 5 times higher than retaining the existing customers). Therefore, predicting the attrition behavior of customers in advance is a significant task. This behavior has triggered most of the researchers to focus on developing the churn prediction model in several industries. Anyhow, in most of the time supervised machine learning techniques have been incorporated in this regard. But in here, an unsupervised machine learning approach has been proposed. A local telecommunication company can be approached for the purpose of conducting this research. Around 10,000 postpaid subscriber details with 20 attributes have been obtained and analyzed during this research. Further, Principal Component Analysis (PCA) and Kmeans clustering algorithm have been utilized with the intention of reducing the dimensionality between features and to find the churners and non-churners respectively. The results obtained from the PCA have revealed that, 16 principal components which represent all the 20 features are considered as most important aspects to cover the entire data. Moreover, totally 6 clusters have been generated and some particular features that tend to show high contributions were identified during the principal component analysis have been analyzed towards each cluster. The proposed approach has finally revealed that out of the 6 clusters three (3) representing 4888 are churners and the other three (3) representing 5112 are non-churners. It could be ensured that, this approach would assist the future researchers to have a promising start for combining the unsupervised technique with the supervised one.Item MLP Model Approach for Driver Fault Identification(4th International Conference on Advances in Computing and Technology (ICACT ‒ 2019), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2019) Ariyathilake, S.N.; Rathnayaka, R.M.K.T.The issue of the traffic accident has gain attention of the globe which has been a major challenge for the sustainable development of transportation and traffic. Crashes are events which occurred by involving different components: Driver, road, environment. Driver identification is directly connected to taking advanced actions on the road accident. Prevention of the road accident is the primary concern and necessary legal actions must be taken for the responsible party of the accident. In order to accurately predict the driver fault regarding an accident, this study aims to identify whether the driver is fault for the accident or not, by using a Multilayer Perceptron (MLP) model. The proposed model accurately predicts the driver fault while ensuring the accuracy of the decision. Proposed Multilayer perceptron model has achieved an accuracy of 97.77% with the accident data. To compare the results of the model, Decision Tree, Linear classifier and DNN classifier has used. Comparative results revealed that the most accurate model as the Multilayer perceptron approach. Necessary sensitivity analysis regarding the MLP was performed to find the best MLP model. Results revealed that by using 500 epochs with RMSprop accuracy was increased. T – Test was performed with 0.05 accuracy level for the selected methods and MLP method outperformed the other techniques. The research will provide the information needed to guide the relevant decision-makers in adopting suitable measures to prevent and to reduce the accident rate.Item Artificial Neural Network based New Hybrid Approach for Forecasting Electricity Demands in Sri Lanka.(Faculty of Computing and Technology, University of Kelaniya, Sri Lanka., 2017) Rathnayaka, R.M.K.T.; Seneviratna, D.M.K.N.The electricity generation and forecasting are playing a significant role to enhance national economic growth. It has a direct impact on both individual’s standards of living and industrial enhancements; especially, it is a prerequisite to enhance industrialization, farming and residential requirements. As a result, most of the countries are allocating a considerable amount for power generation and forecasting from nation’s annual budget. The main objective of this study is to focus on analyzing the electricity demands in Sri Lanka using a new proposed combined hybrid approach based on Artificial Neural Network. The methodology of the study is carried as follows. In the first phase, electricity demand of Sri Lanka is forecasting based on the autoregressive integrated moving average (ARIMA) and Artificial Neural Network (ANN) approaches separately. In the next stage, the new proposed combined approach of ANN and ARIMA (ANN-ARIMA) is applied. According to the Akaike Information Criterion, Schwarz Information Criterion and Hannan Quinn Criterion results, ARIMA(0,1,1) (R-squared : 45%, Durbin-Watson stat: 2.32) and ARIMA (1, 1, 1) (R-squared : 55%, Durbin-Watson stat: 2.03) are best models for forecasting electricity production and electricity consumption under the linear framework respectively. As a next step, proposed ANN-ARIMA hybrid methodology is applied to forecast non-linear composite based on MATLAB training algorithms. Furthermore, the model selection results concluded that, Backpropagation Neural Network (BPNN) (1-4-1) with 0.06 learning rates and BPNN (1-2-1) with 0.04 learning rates are the best one-step-ahead forecasting for electricity production and electricity consumption respectively. According to the empirical results, the electricity production and consumption curves went parallel trend up to 1995. However, after 1995 consumption rate has been increasing rapidly with respect to the production rate. When this is the case until 2020, it will create distortions in the Sri Lankan future. So this study is a good sign for the government and energy sources must be introduced and implemented for national power grid early as possible.