Research Publications

Now showing 1 - 3 of 3

Embargo
Machine Learning-Based Prediction of Settled Water Turbidity for Optimizing Alum Dosage in Drinking Water Treatment
(Association for Computing Machinery, Inc, 2026-06-17) Cooray, S; Fernando, D; Jeyachandran, P; Jayakodi, D; Sumathipala, P; Fousdeen, S; Nawarathne, M
Efficient chemical dosing is essential in drinking water treatment plants to ensure effective turbidity removal while minimizing chemical consumption and operational costs. Conventional alum dosing methods rely on manual jar tests and operator experience, which are time-consuming and often inadequate for handling rapid variations in raw water quality. Key parameters such as turbidity, pH, and electrical conductivity significantly influence the coagulation process, making accurate dosage determination a complex and dynamic challenge. This study proposes a machine learning-based approach to support optimal alum dosage selection by predicting settled water turbidity using historical treatment data. The dataset includes raw water turbidity, pH, conductivity, and applied alum dosage, which are used to train regression models capable of capturing nonlinear relationships between input parameters and treatment performance. The study used 7,975 hourly operational records collected from Ambathale Water Treatment Plant during January-December 2024, and model evaluation was performed using a time-aware 80:20 chronological train-test split. Multiple algorithms, including Gradient Boosting, Random Forest, XGBoost, and LightGBM, were explored alongside feature engineering techniques to improve model effectiveness. Experimental results demonstrate that the Random Forest model achieves superior performance, with an R² score of 0.7909, RMSE of 0.5197, and MAE of 0.3166, indicating strong predictive reliability under varying water quality conditions. The trained model enables simulation of different dosing scenarios, allowing identification of the alum dosage that produces the lowest predicted turbidity under varying raw water conditions.The proposed approach provides a practical decision- support solution for improving treatment efficiency, reducing chemical overuse, and supporting more reliable operation of drinking water treatment processes.
Embargo
LISA : Enhance the explainability of medical images unifying current XAI techniques
(IEEE, 2022-07-18) Abeyagunasekera, S. H. P; Perera, Y; Chamara, K; Kaushalya, U; Sumathipala, P
This work proposed a unified approach to increase the explainability of the predictions made by Convolution Neural Networks (CNNs) on medical images using currently available Explainable Artificial Intelligent (XAI) techniques. This method in-cooperates multiple techniques such as LISA aka Local Interpretable Model Agnostic Explanations (LIME), integrated gradients, Anchors and Shapley Additive Explanations (SHAP) which is Shapley values-based approach to provide explanations for the predictions provided by Blackbox models. This unified method increases the confidence in the black-box model’s decision to be employed in crucial applications under the supervision of human specialists. In this work, a Chest X-ray (CXR) classification model for identifying Covid-19 patients is trained using transfer learning to illustrate the applicability of XAI techniques and the unified method (LISA) to explain model predictions. To derive predictions, an image-net based Inception V2 model is utilized as the transfer learning model.
Embargo
Integrating industrial technologies, tools and practices to the IT curriculum: an innovative course with .NET and java platforms
(acm.org, 2005-10-20) Athauda, R; Kodagoda, N; Wickramaratne, J; Sumathipala, P; Rupasinghe, L; Edirisighe, A; Gamage, A; De Silva, D
Exposure to state-of-art industry technologies, tools and practices by students provide CS/IT graduates highly desirable skills and marketability. A key expectation of the industry from their new cadre is a speedy integration into the business environment resulting in productive work. This usually requires having a sound technological background, a maturity to assess the environment and adapt quickly, and highly-developed soft skills to be productive in a team environment. Incorporating such experience and skills into a CS/IT curriculum is challenging and is still in its infancy stages. We undertook such as an endeavor in integrating .NET into the IT curriculum. Microsoft's .NET platform is becoming increasingly popular in the industry. Incorporating .NET into the undergraduate IT curriculum provides a plethora of skills and increases the employability of our graduates. We integrated .NET without a major revision to the existing curriculum by introducing an optional course in the final year (senior-level) of the IT undergraduate program. In addition to the .NET platform, the course covered the Java platform, which is similar in architecture to .NET. The course emulated an industry-based environment with real-world based assignments, focused on deliverables, used state-of-art IDEs and documentation, and pair programming to create a highly productive environment. The “soft skills” were integrated into the course with a project that implemented a virtual marketplace. Students in groups played different entities in the virtual marketplace and communicated with each other via Web Services. The project provided a virtual business environment and exposure to teamwork, collaboration, competition, negotiating, and creativity skills. Our first offering of the course in semester 1, 2005, attracted 128 students. The course created a highly productive environment throughout the semester. Students completed 7 assignments and the project within the 14-week semester. The initial results are encouraging and provide many insights to CS/IT departments planning to incorporate such courses.

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