Browsing by Author "Munasinghe, R"

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    PublicationOpen Access
    Fast Tail Index Estimation for Power Law Distributions in R
    (2020-06-18) Munasinghe, R; Kossinna, P; Jayasinghe, D; Wijeratne, D
    Power law distributions, in particular Pareto distributions, describe data across diverse areas of study. We have developed a package in R to estimate the tail index for such datasets focusing on speed (in particular with large datasets), keeping in mind ease of use, as well as accuracy. In this short article, we provide a user guide to our package along with the results obtained highlighting the speed advantages of our package.
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    PublicationOpen Access
    Indic language computing
    (Association for Computing Machinery, 2019-10-24) Bhattacharyya, p; Murthy, H; Ranathunga, S; Munasinghe, R
    following the Easter Sunday bomb attacks, the Government of Sri Lanka had to shut down Facebook and YouTube for nine days to stop the spreading of hate speech and false news, posted mainly in the local languages Sinhala and Tamil. This came about simply because these social media platforms did not have the capability to detect and warn about the provocative content. India’s Ministry of Human Resource Development (MHRD) wants lectures on Swayama and NPTELb—the online teaching platforms—to be translated into all Indian languages. Approximately 2.5 million students use the Swayam lectures on computer science alone. The lectures are in English, which students find difficult to understand. A large number of lectures are manually subtitled in English. Automatic speech recognition and machine translation into Indian languages will be great enablers for the marginalized sections of society. Requirements like these are real and abundant.
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    PublicationOpen Access
    Revisiting Feller Diffusion: Derivation and Simulation
    (2019-07-13) Munasinghe, R; Kanthan, L; Kossinna, P
    We propose a simpler derivation of the probability density function of Feller Diffusion using the Fourier Transform and solving the resulting equation via the Method of Characteristics. We also discuss simulation algorithms and confirm key properties related to hitting time probabilities via the simulation
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    PublicationEmbargo
    Sentiment classification of Sinhala content in social media
    (IEEE, 2020-09-24) Jayasuriya, P; Ekanayake, S; Munasinghe, R; Kumarasinghe, B; Weerasinghe, I; Thelijjagoda, S
    In this study, we focus on the classification of Sinhala social media sentiments into positive and negative classes for a particular domain (sports). We have employed machine learning algorithms and lexicon-based sentiment classification methods. We also consider a hybrid approach by constructing an ensemble classifier in which we combine Machine Learning and Lexicon based methods. For individual methods, machine learning algorithms performed best in terms of accuracy. The ensemble classifier was able to improve performance further.
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    PublicationEmbargo
    Sentiment classification of Sinhala content in social media
    (IEEE, 2020-09-24) Jayasuriya, P; Ekanayake, S; Munasinghe, R; Munasinghe, B; Weerasinghe, I; Thelijjagoda, S
    In this study, we focus on the classification of Sinhala social media sentiments into positive and negative classes for a particular domain (sports). We have employed machine learning algorithms and lexicon-based sentiment classification methods. We also consider a hybrid approach by constructing an ensemble classifier in which we combine Machine Learning and Lexicon based methods. For individual methods, machine learning algorithms performed best in terms of accuracy. The ensemble classifier was able to improve performance further.
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    PublicationEmbargo
    Sentiment Classification of Sinhala Content in Social Media: A Comparison between Stemmers and N-gram Features
    (IEEE, 2021-12-09) Jayasuriya, P; Munasinghe, R; Thelijjagoda, S
    Sentiment classification for non-English languages has gained significant attention from researchers in the past few years with the increasing use of non-English scripts and Romanized scripts for expressing sentiments over social media. In this study, we begin by classifying Sinhala sentiments on social media into positive and negative polarity classes using N-gram feature extraction. N-grams are a contiguous sequence of words or characters of a text. Then we focus on improving the classification accuracy by employing different stemming methods. Stemming is generally used to reduce the dimensionality of the feature set - something which needs to be carried out with great care as over reducing feature dimensionality causes the classification accuracy to decrease. Finally, we compare the accuracy and efficiency of N-gram feature extraction and stemming based sentiment analysis models.
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    Sentiment Classification of Sinhala Content in Social Media: An Ensemble Approach
    (IEEE, 2021-12-09) Jayasuriya, P; Munasinghe, R; Thelijjagoda, S
    We focus on the binary classification of Sinhala social media content in the sports domain using machine learning algorithms. In particular, we improve upon the accuracy achieved in a previous study of ours that utilized word and character N-grams. We use the base learners from that study to implement a probability-based stacking ensemble approach. This is done by creating a base learner library of 1066 base learners, using 13 different algorithms and different N-gram feature extraction methods. Different base learner combinations from the library are then stacked together to find the best stacking ensemble model. The best stacking ensemble model achieves an accuracy of 83.8% which is an improvement of over 1.5% of our previous study.
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    PublicationOpen Access
    Usage of Business Analytics and Supply Chain Performance–An Empirical Study of Sri Lankan Apparel Sector
    (2021-02-25) Lakpura, D. D; Anuththara, K. H. G. M; Hansika, P. P. G. C. N; Fernando, K. E. H; Munasinghe, R; Madhavika, W. D. N
    Advances in technology and innovation require companies to embrace these new trends to compete and stay ahead in the business world. In particular, there is a need for companies to incorporate Business Analytics practices within their organizations. Business Analytics consists of two components: Information Systems and Business Process Orientation. This study aims to investigate the impact of the use of Business Analytics on the Supply Chain Performance in apparel companies in Sri Lanka. This research focuses on discussing the objectives developed to achieve the purpose of the study. To achieve this objective, this current study investigates the relationship between the Information System, Supply Chain Performance and the effect of the use of the Information System in the supply chains of Sri Lankan apparel companies. The study uses a quantitative approach. In this study, for quantitative analysis study performs regression analysis and decision tree analysis. This study identifies a positive relationship between the Information System and the Supply Chain Performance. For further future studies, it is advisable to extend this study by examining the performance of medium-and large-scale companies in the country.