Research Papers - Dept of Information Technology

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Start date: 2020Subject: search.filters.subject.Sri Lanka

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Now showing 1 - 8 of 8
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    Comparison of ARIMA and LSTM in Forecasting the Retail Prices of Vegetables in Colombo, Sri Lanka
    (IEEE, 2022-12-09) Fonseka, D.D; Karunasena, A
    Identification of vegetable price trends is important to make better decisions in the production and market. Due to several factors, including seasonality, perishability, an imbalanced supply-demand market, customer choice, and the availability of raw materials, vegetable prices fluctuate quickly and are highly unstable. In this study price prediction was concluded using two models ARIMA and LSTM with retail price data for Cabbage, Carrot, and Green beans in Colombo from 2009 to 2018. According to the decision criteria of RMSE and MAPE, the LSTM model is superior to the ARIMA model in predicting the retail prices of vegetables. There were no studies have focused on predicting prices with novel technology in the Sri Lankan vegetable market. Hence the results of this study can be used to build an advanced forecasting model by the government and decision-makers in agriculture in Sri Lanka.
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    System to Improve the Quality of Water Resources in Sri Lanka Using Machine Learning and Image Processing
    (IEEE, 2022-12-09) Liyanage, M. H. S; Gajanayake, G.M.B. S; Wijewickrama, O; Fernando A, S.D.S. A; Wijendra, D; Gamage, A. I
    Water covers approximately 71% of the earth’s surface, but only 1.2% of it can be used for drinking. However, due to the amount of waste water released into water resources, the presence of harmful microorganisms, and natural occurrences such as eutrophication, even that water cannot be used directly for drinking purposes without purification. One method of purifying water is chlorination. However, if the chlorine level exceeds the standard, it can cause both long-term and short-term illnesses. As a result, a system is imposed to solve four problems: predicting the pH value of chlorinated drinking water, determining the quantification value of active sludge in a wastewater plant, detecting microorganisms in drinking water, and predicting the percentage of eutrophication in a water resource.
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    Machine Learning Based Solution for Improving the Efficiency of Sugar Production in Sri Lanka
    (IEEE, 2022-12-26) Kulasekara, S; Kumarasiri, K; Sirimanna, T; Dissanayake, D; Karunasena, A; Pemadasa, N
    Although sugar is a popularly used commodity in Sri Lanka, sugar manufactured within the country fulfill only a very small portion of the demanded amount. Sugar production is an intricate process which requires a considerable amount of expertise especially in the areas of cultivation, production and revenue prediction which may not exist in novice farmers. This research proposes a methodology which provides novice sugarcane farmers with expert knowledge on four main areas related to farming including weather forecast, sugarcane maturity estimation, production forecast and prediction of return sugarcane amounts from lands. ARIMA model is used for weather forecast whereas machine learning methods and multiple regression models were used for sugarcane maturity estimation and production of forecasts and returns respectively. The final ARIMA time series model was validated with p-value greater than 0.05 for Ljung-Box test with three different lag values. The Support Vector Machines model was identified as the best model with an accuracy of 81.19% for the sugarcane maturity estimation. The SVM model was trained using the HSV and texture features extracted from sugarcane stalk images using image processing techniques. The prediction of sugar production received a testing R-squared score of 87.75% and mean squared error of 0. Prediction of yield received a mean squared error of approximately 0 and R squared score of 98% on test data. The methodology used in this research could be used by novice farmers to increase their cultivation as well as sugar production.
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    Diagnosing autism in low‐income countries: Clinical record‐based analysis in Sri Lanka
    (Wily, 2022-06-16) Samarasinghe, P; Wickramarachchi, C; Peiris, H; Vance, P; Dahanayake, D. M. A.; Kulasekara, V; Nadeeshani, M
    Use of autism diagnosing standards in low-income countries (LICs) are restricted due to the high price and unavailability of trained health professionals. Furthermore, these standards are heavily skewed towards developed countries and LICs are underrepresented. Due to such constraints, many LICs use their own ways of assessing autism. This is the first retrospective study to analyze such local practices in Sri Lanka. The study was conducted at Ward 19B of Lady Ridgeway Hospital (LRH) using the clinical forms filled for diagnosing ASD. In this study, 356 records were analyzed, from which 79.5% were boys and the median age was 33 months. For each child, the clinical form together with the Childhood Autism Rating Scale (CARS) value were recorded. In this study, a Clinically Derived Autism Score (CDAS) is obtained from the clinical forms. Scatter plot and Pearson product moment correlation coefficient were used to benchmark CDAS with CARS, and it was found CDAS to be positively and moderately correlated with CARS. In identifying the significant variables, a logistic regression model was built based on clinically observed data and it evidenced that “Eye Contact,” “Interaction with Others,” “Pointing,” “Flapping of Hands,” “Request for Needs,” “Rotate Wheels,” and “Line up Things” variables as the most significant variables in diagnosing autism. Based on these significant predictors, the classification tree was built. The pruned tree depicts a set of rules, which could be used in similar clinical environments to screen for autism.
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    User-friendly Enhanced Machine Learning-based Railway Management System for Sri Lanka
    (IEEE, 2021-12-06) Mihiranga, G.L.V; Weerasooriya, W. K. M; Palliyaguruge, T. L. P; Gunasekera, P. N. G; Gamage, M. P; Kumari, S. P. K
    The railway service is a convenient and low-cost transport method in Sri Lanka, widely employed by both local and foreign passengers. Major railway lines in Sri Lanka cover unique and very different areas in the country. For example, the Northern province's weather and geography conditions significantly differ from Southern or Central provinces. Majority of the tourists lack understanding in identifying appropriate or attractive places that best suits them, close by to the Railway Stations. Therefore, a passenger needs to spend more time identifying their railway tour destinations. When passengers are booking tickets, even though they are able to reserve seats beforehand, they are unable to reserve a specific seat. Also, there is no process to identify the most suitable seat for them amidst many other travelers, especially if they are travelling alone. Considering the aforementioned, authors propose a more innovative and user-friendly system for the Railway Department of Sri Lanka. Depending on various passenger attributes the system is capable of suggesting a travel plan with railway lines which cover most suitable destination suggestions; identifying the best seats with a relaxing atmosphere; providing an interactive chatbot to satisfy user queries on specific location information; and facility for 24×7 user interaction. A travel plan can save passengers time and allows them to identify the desired railway line and relevant attractions without much hassle. And they are saved of an unpleasant experience through the suggestion of the best seating location. Machine Learning and Deep Learning technologies are used in developing the proposed system.
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    Design and Implementation of Data Warehouse for a Higher Educational Institute in Sri Lanka
    (IEEE, 2021-04) Serasinghe, C. U; Jayakody, C; Dayananda, K. T. M. N; Dinesh Asanka, P. P. G
    In any organization, the leadership is responsible for taking decisions that will lift the said organization to a better place. The problem-solving abilities of the management are mostly depending on the ability to grasp all the required information in a clean and actionable format. Building a well-designed data warehouse leads to answer that problem. When data sourced from different sourcing systems, it's very important that the aggregated data is relevant and supporting the decision-making by the leadership. This study aims at mitigating the issues that are hindering such organizations to make correct decisions.
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    Cicindelinae of Sri Lanka: New record of the arboreal tiger beetle Tricondyla gounellei Horn, 1900
    (NSF, 2020-05-29) Abeywardhana, D. L; Dangalle, C. D; Mallawarachchi, Y
    Information is provided on the newly recorded Tricondyla gounellei Horn, 1900, an arboreal tiger beetle, hitherto known only from Southern India, with this being its fi rst from Sri Lanka. Following fi eld surveys conducted from 2017 to 2019 in forty-one locations in the country, this species was recorded from two locations namely, Vellankulam in Mannar District and Kirinda in Hambantota District. Tricondyla gounellei, closely resembles Tricondyla granulifera Motschulsky, 1857 previously recorded from Sri Lanka. However, T. gounellei can be distinguished from T. granulifera by the smaller body size, short elytra that are narrower in the middle and palpi with black terminal joints which in T. granulifera is red.
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    PublicationOpen Access
    SriHealth: A Single Platform for Meal Plans, Workouts, Yoga Schedules Based on SriLankan Lifestyle
    (2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Anusari, T.H.G.M.; Amarasinghe, B.Y.; Munasinghe, G.K.; Epitawala, E.K.K.N.; Pemadasa, M.G.N.; Weerasinghe, L.
    Food is a fundamental piece of human existence. People have forgotten to follow good eating patterns and exercise goals because of today's fast-paced lifestyles, resulting in malnutrition, which has become one of the most serious public health issues in developing countries, including Sri Lanka. As a result, people are unable to adhere to a probable schedule to satisfy their desires intend for Sri Lankan cuisine. In this study, a mobile-based application named "SriHealth" is developed with an emphasis on Image Processing, Natural Language Processing (NLP), Classification and Regression in Machine Learning techniques. The results obtained show that for classification of food preferences identification produces high accuracy of 87% on Support Vector Machine (SVM) classifier, medical record breakdown comprises of 75% accuracy with Clustering through Logistic Regression, schedule provider consist of an 95% accuracy level in Naïve Bayes algorithm while the calorie counter provides an accuracy level of 73% in MobileNet. The proposed work would identify user food preferences and medical conditions, classify the user, provide the suitable meal plan, exercise and yoga plan schedule on their categorization, and measure the number of calories consumed while assisting them for a healthy life.