Browsing by Author "Kumara, BTGS"

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    Data mining approach for analyzing factors influencing vegetable prices
    (Faculty of Information Technology, University of Moratuwa., 2020-12) Illankoon, IMGL; Kumara, BTGS; Karunananda, AS; Talagala, PD
    Vegetables have a special place in the Sri Lankan economy. The price of vegetables, unlike the prices of other products, changes daily. There are several reasons for this and the examples include environmental conditions, supply variability, demand, festivals and seasonality, social environment, political conditions, etc. The main purpose of this research is to analyze and predict the factors influencing daily vegetable price fluctuations using data mining techniques. In this research, the most influential factors for vegetable prices were classified using the classification algorithms J48, Random Tree, Random Forest, and Support Vector Machine, taking into account the data obtained from the secondary data sources. The highest classification accuracy of 97.7143% was given by the Random Forest algorithm and it also recorded the best values for Precision, Recall, F-measure, and MCC comparing with the other three. Furthermore, it is clear that the Random forest algorithm is the most suitable to predict influential factors and it can be recommended for the purpose.
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    Detection of suicide ideation in twitter using ann
    (Faculty of Information Technology, University of Moratuwa., 2021-12) Yatapala, KDYHT; Kumara, BTGS; Ganegoda, GU; Mahadewa, KT
    Suicide is considered one of the leading problems in the present. Detecting suicide earlier and providing a solution is considered the most successful way to suicide ideation and suicide attempts prevention. At present, online communication channels are used to express the suicidal tendencies of some people. This paper presents a machine learning approach to identify suicide pattern and detect suicide ideation or thoughts by considering online user-generated content with the aim of suicide ideation detection. People who have suicidal ideations, express strong negative feelings. Here, an Artificial Neural Network is used as a machine learning algorithm. To detect ideations of suicide, we generate feature vectors using different techniques including Word2Vec, Doc2Vec, and TF-IDF features. As the online user communication channel, we select Twitter.