International Postgraduate Research Conference (IPRC)
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Item Optimization of SpdK-means Algorithm(Faculty of Graduate Studies, University of Kelaniya, Sri Lanka, 2016) Gunasekara, R.P.T.H.; Wijegunasekara, M.C.; Dias, N.G.J.This study was carried out to enhance the performance of the k-mean data-mining algorithm by using parallel programming methodologies. As a result, the Speedup k-means (SpdK-means) algorithm which is an extension of k-means algorithm was implemented to reduce the cluster building time. Although SpdK-means speed up the cluster building process, the main drawback was that the cumulative cluster density of the created clusters by the SpdK-means algorithm was different from the initial population. This means some elements (data points) were missed out in the clustering process which reduces the cluster quality. The aim of this paper is to discuss how the drawback was identified and how the SpdK-means algorithm was optimized to overcome the identified drawback. The SpdK-means clustering algorithm was applied to three datasets which was gathered from a Ceylon Electricity Board Dataset by changing the number of clusters k. For k=2, 3, 4 did not give any significant difference between the cumulative cluster density and the initial dataset. When the number of clusters were more than 4 (i.e., when k>=5), there was a significant difference on cluster densities. The density of each cluster was recorded and it was identified that the cumulative density of all clusters was different from the initial population. It was identified that about 1% of elements from total population were missing after clusters were formed. To overcome this identified drawback the SpdK-mean clustering algorithm was studied carefully and it was identified that there are elements which had equal distances from several cluster centroids were missed out in intermediate iterations. When an element had an equal distance to two or more centroids the SpdK-means algorithm was unable to identify to which cluster that the element should belong and as a result the element is not included in any cluster. If such element was included into all the clusters that had an equal distance and if this process is repeated to all such elements the cumulative cluster density will be highly increased from the initial population. Therefore, the SpdK-means was optimized by selecting one of the cluster centroids which had equal distance to one element. After many studies of selection methods and their outcomes, it was able to modify the SpdK-means algorithm to find suitable cluster to an equal distance element. Since, an element can belong to any cluster it is not possible give any priority to select a belonging cluster. As all centroids had equal distances from the elements, the algorithm will select one of the centroid from all equal centroids randomly. The developed optimized SpdK-means algorithm successfully solved the identified problem by identifying missing elements and including them in to the correct clusters. By analyzing the iterations when applied to the datasets, the number of iterations was reduced by 20% than the former SpdK-means algorithm. After applying optimized SpdK-means algorithm to above mentioned datasets, it was found that it reduces the cluster building time by 10% to 12% than the SpdK-means algorithm. Therefore, the cluster building time was further reduced than the former SpdK-means algorithm.Item T-Moms for Restaurants(Faculty of Graduate Studies, University of Kelaniya, Sri Lanka, 2016) Medhavi, Y.A.U.; Wijegunasekara, M.C.The aim of this study was to identify the drawbacks of a restaurant order management system and suggest a solution. Several such systems were studied and it was identified that customers waiting time to receive an order is considerably high. This is because during peak hours the waiter staff is not sufficient and the service offered is not to the required standard. In addition, the paper menus can be flimsy, hard to navigate, and outdated. To reduce customer’s wait times, management must ensure sufficient staff to present during peak hours and that they are properly trained to provide excellent customer service. These staffing issues can lead to substantial costs for the business. As a result, the Tablet based Menu and Order Management System (T-MOMS) was implemented to resolve these problems using mobile devices. The T-MOMS contains four systems, a mobile application for customers and three web based systems for the admin panel, kitchen and cashier. The order is taken by a mobile device namely, a tablet placed on the restaurant table which acts as a waiter. The mobile application is started by a waiter by logging into the system and assigning the table number and a waiter identification. The waiter identification and table number are saved in the application until that particular waiter logs out. The mobile application has four subsystems namely, display subsystem, assistance subsystem, commenting subsystem and ordering subsystem. The display subsystem displays the complete restaurant menu by categories, special offers’ information and allows the customer to browse all the currently available menu items by category. The assistance subsystem allows the customer to call a waiter for any assistance needed. The commenting subsystem allows customers to create user accounts for adding comments and share experience on Facebook/Twitter. The ordering subsystem allows to select the desired items and make the order. Once the customer makes the order, first he will be able to view the order information that he has ordered including the payment with/without tax and service charge. After the customer confirms the order, the order is transmitted to the kitchen department via Internet for meal preparation. The kitchen web system displays all order information that are received from the tablets. This include the customer details, table number, the waiter identification and the details of the order. After the order is prepared, the waiter will deliver the order to the customer. At the same time, the cashier web system receives the details of the delivered order and the bill is prepared. The web based admin panel system allows the restaurant’s management to add/view/remove/ update menu items and waiters, view reservation information and their cooking status/payment status, update service charge/tax, viewing revenue information over a time period. The T-MOMS system consists of a central server and a database. All ordering and expenditure information is stored in a central database. Eclipse and PHPStorm used as the IDEs. Mainly used languages are HTML, JavaScript, PHP, JAVA, XML. The menu application is designed to be used on 7" tablets as well as it will be supported on smaller screen sizes. As future development, features such as restaurant statistics should be implemented, paying the bill directly through the menu application should be created.Item A Study on Loan Performance Using Data Mining Techniques(Faculty of Graduate Studies, University of Kelaniya, 2015) Thisara, E.B.; Wijegunasekara, M.C.Most of the modern financial companies offer loans to customers in order to build up their own business. Such companies have a major problem when they recover the loan as the customers do not pay the installments according to the signed contract. It is crucial to determine/create the appropriate strategies and to identify the risk free customers as there is high potential of non-performing loans. In order to predict the risk factors that affect to non-performing loan, Data Mining techniques were considered. This research discovered the factors/reasons for non-performing loan using the data from a reputed Finance Company. This research focused on eighteen attributes which were referred to as factors affecting a nonperforming loan state and the dataset contained with 30% of test data and 70% of training data from 750 records. Among those attributes eleven key attributes namely: Age, Area, Branch Name, Customer Job, Income, Loan State, Mortgage, Number of Terms, Overdue days, Product Type and Interest Rate were selected to create the data mining models. The considered mining models were namely: Neural Networks (NN), Decision Trees (DT) and Clustering (CL). These models were created using the Business Intelligence tool and the database was created in SQL Server Management Studio 2008R2. The predicted probabilities (as a percentage) of Neural Networks, Decision Trees and Clustering models were 1.57%, 0.44% and 10.46% for non-performing loan state respectively. As the Clustering Model had the highest value it was chosen as the best algorithm to evaluate loan state by using Microsoft clustering method. The Clustering model was given ten clusters numbered from 1 to 10 and five clusters namely: 3, 6, 8, 9 and 10 were identified as the most inclined towards the non-performing loan state by comparative analysis. The predicted probabilities of selected clusters were 23%, 41%, 32%, 23% and 35% respectively and cluster number 6 showed a highest value and cluster number 10 showed the next highest value. Based on cluster performance, numbers 1, 2, 4, 5, 7 had a high probability of becoming performing loan and thus were not included in the analysis. According to the states of attributes within each cluster profiles Product Type, Customer Job, Mortgage, Income, Number of Terms and Interest Rate were identified and shortlisted as the factors affecting the nonperforming loan state most. The research identified that if the customer is self-employed or individual, a small property owner, or having a low income and depending on the type of mortgage (building, vehicle or non-mortgage) the loan tend to be non-performing. The longer duration for loan repayment or higher interest rates will also cause a loan to be non-performing. According to the above results it can be concluded that the high interest loans provided for the unemployed customers or customers with low income have a higher potential to be non-performing and hence resulting in a monetary loss for the financial company. Therefore a financial company will be able to improve its profits if they are more concerned about such customers and undertake suitable decisions. The model will support the financial sector in identifying the amount of loans that could be transformed into the non-performing state. Therefore the findings of this research will benefit the financial industry to reduce the risk of granting loans when providing loans in future.