Browsing by Author "Rajapakshe, S"

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item: Conference-Full-text
Automated question and answer generating system for educational platforms
(Faculty of Information Technology, University of Moratuwa., 2021-12) Thiruvanantharajah, M; Hangarangoda, N; Rajapakshe, S; IT; Ganegoda, GU; Mahadewa, KT
Learning through the web gets to be well known which encourages learners to learn any kind of stuff at any time from the internet assets. In exam preparing questions and answering is have moved into the technology world. In Many industries, more activities have begun to shift as a result of the increased changes brought about by the Covid-19 virus to people's usual livelihoods, and one significant component whose technologization has created concerns is education. This paper presents a novel system that has been introduced to improve the standards of instructing via virtual and non-virtual platforms by ensuring that both the educational staff and the students are provided with the same level of understanding of their education. The support system ensures that the students and educational staff are provided with an automatic question and answer generation mechanism which will thereby improve the quality of education by presenting a standardized method of preparing questions to the educational staff, while similarly providing a better opportunity to improve study methods for the students.
item: Thesis-Full-text
Developing a tool to manage the credit risk using data mining
Rajapakshe, S; Premarathne, S
Ceasing vehicles are affecting to the credit liquidity of the leasing company. This research has been conducted to develop a tool to manage credit risk in leasing companies using data mining. This tool will predict the ability of recoverability of the loan and determine the most suitable plan for the customer. It is hypothesis that, using data mining technology, the credit risk of leasing companies can be managed. Past dataset from the leasing company has been used to create the data mining model. When a customer comes to lease a vehicle, decision maker will get the information from the customer and enter to the system as inputs then the system will predict the tendency of recoverability of the loan and will give the suitable plans for the customer after evaluating with the previously generated model. This system generated details will support the decision maker to take his decision. The overall design includes frontend software and it is connected to the WEKA API which issued under the GNU General Public License. The data model that is used in this tool to manage credit risk in leasing companies has been tested by considering a data collected from the medium scale leasing company in Sri Lanka.