Research Papers - Dept of Information Technology

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/593

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    A Machine Learning Approach to Predict Default Lease Cases in Sri Lankan Financial Institutions
    (IEEE, 2022-12-26) Perera, V. A. S.; Kavirathne, G. P. R. A.; Karunathunge, L. C. R.; Dewapura, B. N.; Karunasena, A.; Pemadasa, M. G. N. M.
    The economic growth of a country can be aided by a strong financial services industry. Therefore, financial companies play a vital role in today’s society. However, by providing credit facilities, they expose themselves to a significant amount of risks, since most of them lack a proper strategy to identify whether the customer is reliable and capable of paying back on time. Hence, it is widely acknowledged that having a proper strategy in place to manage and lessen the credit risks that these companies face is more beneficial, rather than relying on traditional manual techniques. This study is intended to propose a machine learning-based solution to predict possible financial lease defaults beforehand. The dataset used in this work was obtained from a leading finance company in Sri Lanka, where the data were related to leasing contracts and their equipment. According to the final results of this study, a deep learning model implemented using an Artificial Neural Network, which was compared against several other machine learning models, is the best to predict default lease cases in Sri Lankan financial institutions. The finalized model provides 93.93% of classification accuracy, 85.49% of F-measure, 87.69% of AUROC score, and 80.41% of Kappa score.
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    Ontology Based Question Answering System for Sri Lankan Online School Education
    (IEEE, 2022-10-04) Jayabahu, J.M.G.R.; Rajapaksha, U.U.S.
    Today, distance education is one of the world’s most popular forms of education, and there are several opportunities for students to receive education online. Here, ontology can be considered one of the leading knowledge representation ways in e-learning systems. This research addressed students’ learning difficulties in Sri Lankan online education during the past two years. Students had to learn from home via online video conferences or audio series taught by teachers. However, students could not learn by asking questions or referring to the library materials to improve their self-studying knowledge. To overcome this issue, this research developed an ontology for school children in Sri Lanka, focusing on their IT syllabus and improving their self-education knowledge. This aims to provide personalized content while improving information searching. Students can ask questions from UI, and questions are taken as an input parameter and generate a query while cleansing for matching processes. Answers are generated by connecting to the index database and ontology repository, and the end output is displayed in the user interface. In the evaluation, it was targeted to categorize the questions according to relevant components, and the research shows the questions that are categorized into relevant categories while enhancing the performance.
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    Analysis on the Risk and the Categorization on Test Automation in Sri Lankan Software Industry
    (IEEE, 2021-12-09) Sundaralingam, S; Rajapaksha, S. K
    Delivering quality software to customer is the key objective of software industry. One of the essential fragment of life cycle of software is software testing. In software testing test automation is playing a major role. If test automation cannot be practiced in proper way the delivery of the software quality would impact directly and leads to loss of customer, which is a failure of business. Test automation has several problems which needs to address in each stage. Test automation cause several issues when execute test automation in a company. All these issues need to be handled by different people, therefore initially issues need to be identified and classified and then solve properly. This research is to identify the improvements to categorize the problems automatically and find the solution for the problem in test automation process and hence to practice the test automation in healthier way in order to achieve better software quality. Test automation issue are analyzed and the solutions are proposed. On which stage, the test automation is causing problems and how to solve them are recommend in this research, Test automation issues are categorized and under relevant category therefore issues can be solved speedily. The issues are passed as sentence and they are categorized under the relevant category to fix them quickly. The sentences are preprocessed and conducted feature selection using filter methods and predict under appropriate category. The issue has been cleaned in preprocess stage. Implemented LSTM base algorithm using filter method to categorize the issues. In this research an implementation to categorize test automation problems are formed. Recommendation and solutions are proposed on test automation which would aid to practice test automation in better way and that would leads to better software quality delivery.