Browsing by Author "Hakmanage, N. M."

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Analysis of groundwater used in areas beyond the National Water Board distribution network in Ratnapura
(Faculty of Science, University of Kelaniya Sri Lanka, 2023) Karunasena K, . K. A. D. A.; de Silva, D. S. M.; Hakmanage, N. M.
Groundwater is the main source of drinking water in selected areas in Ratnapura where municipal water is not accessible. In a majority of households, it is consumed without any treatment. Waterborne diseases such as dysentery and typhoid fever may spread as a result of microbial pathogens. Chemical contaminants may also result in kidney-related issues. The present study was conducted to evaluate the quality of groundwater in three Grama Niladhari divisions around Ratnapura urban area. Amuwala, Kahengama South and Gonakumbura divisions were selected, and twenty wells were sampled from each division for three months as replicates. Measured parameters included total Coliforms, Escherichia coli, color, turbidity, pH, electrical conductivity, chloride, total alkalinity, total hardness, total iron, sulfate and total dissolved solids (TDS). Onesample t-test was performed at a 5% level of significance to assess the deviation of each parameter from Sri Lanka water quality standards. The results revealed that all water sources in the study were microbiologically contaminated throughout the sampling period. All of the physical and chemical water quality parameters were within the limits of Sri Lanka Standards (SLS) 614: 2013 except the pH level which was below the SLS requirement. The turbidity level was statistically significant at the SLS median value in Kahengama and Gonakumbura. Since the presence of E. coli bacteria indicates potential fecal contamination in the water, public awareness programmes are needed to educate consumers on the importance of consuming boiled water. The low pH issue can also be resolved by using pH adjusting water filters. The municipal distribution lines have to be extended further permitting access to disinfected potable water to a greater number of consumers.
Artificial intelligence and machine learning-powered personalized mathematics learning system for ordinary level students in Sri Lanka
(Faculty of Science, University of Kelaniya Sri Lanka, 2024) Abeynayake, D. N.; Hakmanage, N. M.; Chamini, A. M. L.
In today’s digital age, integrating technology into education has revolutionized how we learn, making complex subjects such as mathematics more accessible and engaging. Mathematics remains a significant challenge for Sri Lankan Ordinary Level (O/L) students, with high failure rates, limited personalized attention, traditional teaching methods, resource limitations, and language barriers. In response to these challenges, this study is focused on developing an artificial intelligence (AI) and machine learning (ML)-powered personalized mathematics learning system for grade 10 and grade 11 (O/L) students in Sri Lanka. The system is focused on addressing the individual learning needs and helping them improve math with diversified topics, incorporating a chatbot that offers immediate assistance to learners and a feedback mechanism embedded for checking the progress of the learners regularly. The study aims at developing ways of measuring the effectiveness of personalized learning paths in improving students’ mastery of specific mathematical concepts. Furthermore, it explores how AI and ML-based approaches correlate with pupils’ academic achievements by considering motivation levels in mathematics. Additionally, the study aims on developing tutoring functionalities within the system to foster improvements in student satisfaction and performance. Agile methodology was adopted for this study, enabling iterative development to improve the system throughout the project. The learning system underwent each development cycle, focusing on specific features and user feedback, which led to improvements in functionality and user experience. The system was mainly developed using the MERN stack, which comprises MongoDB for data storage, Express.js for server-side logic, React for the frontend, and Node.js for the backend. Furthermore, a linear regression machine learning model was trained for the real-time feedback system. Information on which topics students find challenging was gathered by surveying grade 10 and grade 11 students, both in person and online via Google Forms, with questions asking students to rate their difficulty level with various topics. In order to accommodate all the students, the surveys were conducted in both Sinhala and English languages, and the data gathered was used to train the chatbot using the RASA framework. The system evaluates students' basic math proficiency through a placement test and recommends a personalized learning package based on the test results. The system has shown significant improvements in math concept understanding and performance among 90% of ordinary-level students in Sri Lanka. The system's adaptability and real-time feedback, chatbot providing instant support, offer a viable solution to conventional teaching methods and improving education. Limitations include broader testing and scalability, with future work focusing on improving the AI model and feedback system. AI and MLpowered personalized systems are recommended to be used in schools to help each student learn better.
Association between backache related quality of life and serum creatinine in Chronic Kidney Disease of unknown aetiology (CKDu) patients in the North Central Province, Sri Lanka
(4th International Research Symposium on Pure and Applied Sciences, Faculty of Science, University of Kelaniya, Sri Lanka, 2019) Aslam, F.; Hakmanage, N. M.; Suriyakumara, V.; Sandaruwan, L.; Kumarasinghe, N.
Chronic kidney disease of unknown aetiology (CKDu) is a slow progressive disease that cannot be detected until the later stages. This is commonly described among the agricultural communities in Sri Lanka. CKDu has been a burden over the past two decades and mainly affects the North Central province. This disease is spreading towards the Southern, North Western and Central provinces. CKDu is staged using the estimated-glomerular filtration rate (e-GFR) which is the standard test performed under the WHO guidelines using serum creatinine (SCr) measurement. Backache has been identified as a common symptom among patients suffering from any form of CKDu. It is postulated that backache can be used as an indirect measure to gain an insight about the patients’ health status. The pain measurement also allows an overall assessment of the individuals’ quality of life affecting activities of daily living and which can be used to measure total outcome of the disease. Using an interviewer-based questionnaire, backache was assessed among patients with CKDu. Roland-Morris low back pain and disability questionnaire (RMQ) was used, which had 24 questions and given scores ranging from 0-24. In addition to RMQ, five questions related to the body and pain derived from the KDQOL questionnaire (a standard tool used to determine quality of life (QOL) in kidney disease affected patients) was also used. The scores were combined and evaluated into four main types of backaches: no backache, intermittent, nociceptive and neuropathic backache. The questionnaire was categorized into four variables Correlation statistics were applied to determine the relationship between backache and SCr values in CKDu. IBM SPSS version 23 was used for the statistical analysis. Based on results, a predictive model was designed to understand the correlation between the severity of backache and serum creatinine in CKDu patients. A sample of 75 patients with CKDu were included in the analysis. R-square of 80.9% was observed only in RMQ model. The ANOVA test reported how well the regression equation fits the data. There was a positive relationship between SCr and severity of backache (p<0.001). Using the available findings, a predictive model was designed to understand the severity of backache with serum creatinine in CKDu patients. This may be used in early interventions to improve QOL. However, future studies and larger sample size are required to establish these findings
Route optimization of solid waste collection in Gampaha
(Research Symposium on Pure and Applied Sciences, 2018 Faculty of Science, University of Kelaniya, Sri Lanka, 2018) Hakmanage, N. M.; Jayasundara, D. D. M.
For this study, we have selected Gampaha municipal area. According to the estimates and the enumerated population 2012 (census) in Sri Lanka, among the 25 districts, the highest population is reported from Colombo district. The second highest population is reported from Gampaha district. Even though there are several waste management problems, before a huge disaster due to unsustainable disposal waste in second populated district in Sri Lanka, we propose an optimal waste collecting path. The main objective of this research is to optimize Municipal Solid Waste (MSW) collection routes using mathematical model to maximize collected solid waste amount and minimize the cost and collection time. To use route optimization process, data related in collection process such as type of vehicles used to waste collection and capacity, the amount of solid waste production and the number of inhabitants for each route are essential. Lack of such data leads us to estimate the solid waste production amount per each route by considering the number of houses/buildings in each route. For 10 sections in the Gampaha Municipal area, the modified maximum flow amount technique and the shortest path model were used to optimize solid waste collection process with minimum cost. The Geographic Information System (GIS) and Google map were used to identify routes, count number of houses/buildings in each route, and to find route distance between each connected junctions/intersections. Total traveled distance for the waste collection at each day was calculated as 858 km after finding the optimum routes by proposed model which is more than 10% efficient compared to the current traveled distance. In the current system, 10 vehicles are being used for collection whereas proposed model needs only 8 vehicles. According to this study, 14.2% and 20% thrift can be obtained via distance and vehicle allocation respectively. The consequences of the reductions in travelled time, total time and travelled distance were savings in costs related to fuel consumption.