Browsing by Author "Kasthurirathne, D."

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Generating 2.5D Motion Graphics from 2D Designs
(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Ranawake, I.; Guruge, S.; Ahamed, A.; Kasthurirathne, D.
As of the year 2020 , the video production industry is worth 31 billion dollars in the United States alone, with more than 6000 businesses and 57000 employees, and keeps growing. The global computer animation market size is anticipated to reach USD 28.30 billion by 2025, according to Grand View Research, Inc. Such significant growth demands the tech industry to introduce better tools for making animations. In this paper, we propose our contribution, specifically for User Interface designers in the field of motion graphics. UI/UX design is found to be one of the top 5 most trending opportunities for motion designers, and our proposed system allows them to generate a 2.5D animation based on a 2D Futuristic User Interface (FUI) design. The ultimate goal is to reduce the production time of FUI animations and minimize the cost of responding to the client’s changes. Obtaining clients’ feedback directly on animations rather than still images would improve the client’s involvement in production, resulting in greater confidence and loyalty. We implement several image processing techniques such as thinning and pixel clustering for pre-processing the 2D designs to segment the given design into an array of atomic shapes. Since the thinned shapes ensure that any pixel in the design does not have adjacent pixels which are also adjacent to each other, it is possible to utilize mathematical means to approximate the shapes. Our system converts a given 2D design to a collection of animated lines and arcs distributed in 3D space that eventually can be exported to the industry-standard tool, Adobe After Effects.
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Prediction of CKDu using KDQOL score, Ankle Swelling and Risk Factor Analysis using Neural Networks
(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Lokuarachchi, D.N.; Manoj, J.V.T.; Weerasooriya, M.N.H.; Waseem, M.N.M.; Aslam, F.; Kumarasinghe, N.; Kasthurirathne, D.
Chronic Kidney disease (Chronic Kidney Disease (CKD)) is a type of kidney disease where gradual loss of kidney function occurs over a period of months to years. But, when CKD cannot identify a manner or causation of the disease or set of causes it is known as Chronic Kidney disease with unknown etiology (CKDu). There are several factors to be considered when analyzing the main causes for CKDu such as socio-economic, environmental, meteorological and health aspects in relation to the CKDu in Sri Lanka. In this research work, identification of CKDu has been done using the relationship of the Kidney Disease Quality of Life (KDQOL) score, ankle swelling with the serum creatinine level of blood and considering risk factors. This research has been done using three major branches of Artificial Intelligence namely neural networks, convolutional neural networks and machine learning. The relationship between the mentioned factors and CKDu has been identified. The sensitivity of 77.27% and a specificity of 89.28% have been marked for the detection of CKDu related to ankle swelling.