Browsing by Author "Subasinghe, A"

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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, N
In 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.
Open Access
Real-time Multi-spectral Iris Extraction in Diversified Eye Images Utilizing Convolutional Neural Networks
(IEEE, 2024-07-03) Rathnayake, R; Madhushan, N; Jeeva, A; Darshani, D; Pathirana, I; Ghosh, S; Subasinghe, A; Silva, B N; Wijenayake, U
Iris extraction has gained prominence due to its application versatility across many domains. However, achieving real-time iris extraction poses challenges due to several factors. Learning-based algorithms outperform non-learning-based iris extraction methods, delivering superior accuracy and performance. In response, this article proposes a Convolutional Neural Networks (CNN)-based, accurate direct iris extraction mechanism for a broad spectrum of eye images. The innovation of our approach lies in its proficiency with varied image types, including those where the iris is partially obscured by the eyelid. We enhance the method’s reliability by introducing a modified Circular Hough Transform (CHT). Extensive testing demonstrates our method’s excellent real-time performance across diverse image types, even under challenging conditions. These findings underscore the proposed method’s potential as a cost-effective and computationally efficient solution for real-time iris extraction in varied application domains.