MPhil Candidate - Faculty of Engineering, University of Peradeniya, Sri Lanka
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Citations since 2017
9 Research Items
Explainable AI Classical Machine Learning models
March 2021 - September 2021
Octave | John keells Holdings PLC
- Data Engineering: Pipeline Building, PII service implementation Data Science: Dashboard building, Propensity model building, Solution design for use cases Analytic Delivery: DevOps and sprint coordination, Pilot design, Campaign analysis, Project planning, Problem-solving with business units, Coordinate stakeholders, Documentation of project from ideation to pilot.
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 sev...
This study used explainable machine learning (XML), a new branch of Machine Learning (ML), to elucidate how ML models make predictions. Three tree-based regression models, Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boost (XGB), were used to predict the normalized mean (C p,mean), fluctuating (C p,rms), minimum (C p,min), and maxim...
Predicting bulk-average velocity (UB) in open channels with rigid vegetation is complicated due to the non-linear nature of parameters. Despite higher accuracy, existing regression models fail to highlight the feature importance or causality of respective predictions. Therefore, we propose a method to predict UB and the friction factor in the surfa...
Conventional methods of estimating pressure coefficients of buildings retain time and cost constraints. Recently, machine learning (ML) has been successfully established to predict wind pressure coefficients. However, regardless of the accuracy, ML models are incompetent in providing end-users’ confidence as a result of the black-box nature of pred...
Conventional methods of estimating pressure coefficients of buildings retain time and cost constraints. Recently, machine learning (ML) has been successfully established to predict wind pressure coefficients. However, regardless of the accuracy, ML models are incompe-tent in providing end-users confidence as a result of the black-box nature of pred...
Machine learning (ML) techniques are often employed for the accurate prediction of the com-pressive strength of concrete. Despite higher accuracy, previous ML models failed to interpret the rationale behind predictions. Model interpretability is essential to appeal to the interest of domain experts. Therefore, overcoming research gaps identified, t...
Traditional methods of pressure measurement of buildings are costly and time-consuming. As an alternative to the traditional methods, this study developed a fast and computationally economical machine learning-based model to predict surface-averaged external pressure coefficients of a building with an unconventional configuration using three tree-b...
Chronic Kidney Disease (CKD) or chronic renal disease has become a major issue with a steady growth rate. A person can only survive without kidneys for an average time of 18 days, which makes a huge demand for a kidney transplant and Dialysis. It is important to have effective methods for early prediction of CKD. Machine learning methods are effect...