Browsing by Author "Ekanayake, I. U"

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Open Access
Predicting Bulk Average Velocity with Rigid Vegetation in Open Channels Using Tree-Based Machine Learning: A Novel Approach Using Explainable Artificial Intelligence
(MDPI, 2022-06-10) Meddage, D. P. P; Ekanayake, I. U; Herath, S; Gobirahavan, R; Muttil, N; Rathnayake, U
Predicting the bulk-average velocity (UB) in open channels with rigid vegetation is complicated due to the non-linear nature of the parameters. Despite their higher accuracy, existing regression models fail to highlight the feature importance or causality of the respective predictions. Therefore, we propose a method to predict UB and the friction factor in the surface layer (fS) using tree-based machine learning (ML) models (decision tree, extra tree, and XGBoost). Further, Shapley Additive exPlanation (SHAP) was used to interpret the ML predictions. The comparison emphasized that the XGBoost model is superior in predicting UB (R = 0.984) and fS (R = 0.92) relative to the existing regression models. SHAP revealed the underlying reasoning behind predictions, the dependence of predictions, and feature importance. Interestingly, SHAP adheres to what is generally observed in complex flow behavior, thus, improving trust in predictions
Embargo
Segmentation and significance of herniation measurement using Lumbar Intervertebral Discs from the Axial View
(IEEE, 2022-10-04) Siriwardhana, Y; Karunarathna, D; Ekanayake, I. U
According to statistics, more than 60% of people suffer lower back pain at a certain time in their lives. Disc hernias are the most common cause of lower back pain, and the lumbar spine is responsible for more than 95% of all herniated discs. Generally, radiologists study the MRI during the clinical phase to detect a disc hernia. There could be several cases to evaluate, leaving the doctors to cogitate and envisage. Medical image segmentation aids in the diagnosis of spinal pathology, studying the anatomical structures, surgical procedures, and the evaluation of various treatments. However, manual segmentation of medical images necessitates a significant amount of time, effort, and discipline on the part of domain experts. This research study describes a framework that automates the segmentation of lumbar intervertebral discs using MRI images. Through this system, we can detect minor changes at the pixel level that are impossible to identify with the naked eye. We used convolutional neural networks with the UNet architecture to achieve the semantic segmentation process. The segmentations were evaluated using the Jacquard index and the dice coefficient.