Research Publications Authored by SLIIT Staff
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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.
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Publication Embargo Software Complexity Reduction through the Process Automation in Software Development Life Cycle(IEEE, 2021-11-29) Wijendra, D; Hewagamage, K. PNumerous software complexity metrics have been introduced to quantify the software complexity in terms of different attributes considered in its written source code. Although the complexity determination is bounded with its source code, it should be expressed beyond its code base level, since the software is implemented as a combination of different phrases inside the Software Developments Life Cycle. The automation of the processes involved in software implementation procedure will mitigate the human effort taken during the phrases, resulting that the overall complexity of the software will also be reduced. The proposed system has the capability to demonstrate the requirement analysis, design, defects tracking, quality analysis and the complexity computation with respective to the different complexity metrics without restraining the software complexity evaluation into several quality attributes within the source code.Publication Embargo Moderate Automobile Accident Claim Process Automation Using Machine Learning(IEEE, 2021-01-27) Imaam, F; Subasinghe, A; Kasthuriarachchi, H; Fernando, S; Haddela, P. S; Pemadasa, NIn modern-day, traditional automobile accident claim process struggles to keep up with the recurring automobile accidents and furthermore, the claim itself is a critical point in which the policyholder may decide to switch to a different automobile insurance provider. In this paper, the authors present a system which can be used to automate the processing of claims for automobiles which were involved in less severe accidents in a much quicker manner. The presented system comprises of four components, each with a model developed using computer vision or machine learning techniques to facilitate the automation process. The models are built and fine-tuned using transfer learning and ensemble learning techniques in order to determine the damaged component of the automobile, determine the make and model of the automobile, compute an accurate repair estimate and also compute the likeliness of the policyholder may churn, to ensure that the policyholder is satisfied with the appraised amount and will be retained by the insurance provider.