Faculty of Computing

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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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    Forecasting Model of Combining Mini Batch K Means and Kohonen Maps to Cluster and Evaluate Gait Kinematics Data
    (IEEE, 2022-10-04) Indumini, U; Jayakody, A
    When people are getting old, some gait abnormalities may have happened in their walking patterns. It means, there may be slight differences in their physical performance. Due to the complexity of that evaluation, a machine learning algorithm can be used to cluster the gait patterns. Kohonen Maps (KM) and mini-batch k-means (MBKM) have been combined to cluster the gait parameters according to the age groups to identify the principal gait characteristics which are affected to the walking pattern. Dataset is consisting of 180 gait data based on the data which have been gained through the inertial measurement unit (IMU). When analysing the results, the proposed algorithm is showing low computational cost and time which is more efficient. As well the results have been proved that the cadence is the most important and affected gait parameter when caused to a walking pattern of a person when he or she is getting older. These results provide clues for the health professionals to identify and evaluate the difficulties of walking patterns of patients according to age.
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    Realty Scout – Smart System for Real Estate Analysis & Forecasting with Interactive User Interface
    (IEEE, 2022-07-18) Hapuarachchi, H. A. V. P. U; Manoratne, M. D; Gamlath, K. G. B. K; Vithane, S. G. G; Sriyaratna, D; Ravi Supunya, N. H. P
    The real estate industry is one of the highest income generating sources in the country. As the country moves toward a highly diversified economy, the role of real estate has become a more important part of the country’s economy. However, the state of the local real estate industry is yet to improve and is currently lagging the technology curve. As a result of this issue, useful information is not made available to the end-users. Therefore, the real estate industry needs to improve its adoption of ongoing technologies to move from traditional to smart real estate industry. Therefore, we developed a Smart Real estate system called "Realty Scout" which can analyze and forecast real estate information accurately. The "Realty Scout" is implemented with a highly interactive view of the properties with a given virtual tour for the users to enhance the user experience. This smart real estate system also collects data on property values, in addition to a trained data set, to forecast future property values. Certain machine learning algorithms are used in the backend to generate future values. An accurate and fast prediction of the real estate value is important to buyers, sellers, and other stakeholders. Furthermore, by gathering users’ personal information and tracking their search history through the system, the system recommends properties to users based on collected data. As potential users of the system, they can gain an advantage from this feature by finding their desired property without spending more time. In addition, this system aimed to give advanced property filtrations options to the user. Building up a smart system for the real estate industry would be an advantage for all stakeholders who are actively engaged with the real estate industry.
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    Performance evaluation on machine learning classification techniques for disease classification and forecasting through data analytics for chronic kidney disease (CKD)
    (IEEE, 2017-10-23) Gunarathne, W. H. S. D; Perera, K. D. M; Kahandawaarachchi, K. A. D. C. P
    Chronic Kidney Disease (CKD) is considered as kidney damage which lasts longer than three months. In Sri Lanka, CKD has become a severe problem in the present days due to CKD of unknown aetiology (CKDu) that can be seen popularly in North Central Province. Identifying CKD in the initial stage is important to provide necessary treatments to prevent or cure the disease. In this work main focus is on predicting the patient's status of CKD or non CKD. To predict the value in machine learning classification algorithms have been used. Classification models have been built with different classification algorithms will predict the CKD and non CKD status of the patient. These models have applied on recently collected CKD dataset downloaded from the UCI repository with 400 data records and 25 attributes. Results of different models are compared. From the comparison it has been observed that the model with Multiclass Decision forest algorithm performed best with an accuracy of 99.1% for the reduced dataset with the 14 attributes.
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    Performance evaluation on machine learning classification techniques for disease classification and forecasting through data analytics for Chronic Kidney Disease (CKD)
    (IEEE, 2017-10-23) Gunarathne, W. H. S. D; Perera, K. D. M; Kahandawaarachchi, K. A. D. C. P
    Chronic Kidney Disease (CKD) is considered as kidney damage which lasts longer than three months. In Sri Lanka, CKD has become a severe problem in the present days due to CKD of unknown aetiology (CKDu) that can be seen popularly in North Central Province. Identifying CKD in the initial stage is important to provide necessary treatments to prevent or cure the disease. In this work main focus is on predicting the patient's status of CKD or non CKD. To predict the value in machine learning classification algorithms have been used. Classification models have been built with different classification algorithms will predict the CKD and non CKD status of the patient. These models have applied on recently collected CKD dataset downloaded from the UCI repository with 400 data records and 25 attributes. Results of different models are compared. From the comparison it has been observed that the model with Multiclass Decision forest algorithm performed best with an accuracy of 99.1% for the reduced dataset with the 14 attributes.