Smart Computing and Systems Engineering - 2023 (SCSE 2023)

Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/27032

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Author: search.filters.author.Asanka, Dinesh

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    Sentiment Reason Mining Framework for Analyzing Twitter Discourse on Critical Issues in US Healthcare Industry
    (Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Edirisinghe, Rasika; Asanka, Dinesh
    This research study employs machine learning and textual analysis techniques to examine the US healthcare system through the analysis of Twitter data. By leveraging domain-specific keywords and hashtags, a customized data collection algorithm is utilized to gather a substantial dataset of tweets related to #medicaid and Medicaid. The collected tweets undergo a comprehensive analysis using sentiment analysis, sentiment spike detection, keyword extraction, k-means clustering, topic modeling, and textual association. The study aims to extract insights and identify critical issues hindering access to quality healthcare. The findings have implications for marketing strategies, enabling companies to better align their offerings with customer needs. Additionally, policymakers and healthcare organizations can benefit from the insights gathered, gaining valuable knowledge about the public's concerns, preferences, and satisfaction with US healthcare services and systems. By employing machine learning and textual analysis techniques, this research contributes to a deeper understanding of public sentiment and provides data-driven insights to address challenges in the healthcare domain.
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    Defaulter Prediction in the Fixed-line Telecommunication Sector Using Machine Learning
    (Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Ginige, Sachini; Rajapakse, Chathura; Asanka, Dinesh; Mahanama, Thilini
    In the modern connected era, the telecommunications sector plays a critical role in enabling efficient business operations across all industries. However, defaulting customers who fail to pay their dues after consuming services remain a significant challenge in the industry. Defaulters pose a risk to service providers, calling for measures to lessen both the probability of occurrence as well as its impact. Early identification of defaulters through prediction is a possible solution that enables proactive measures to mitigate the risk. However, the nature of the fixed-line product segment poses additional constraints in identifying defaulters, highlighting an existing knowledge gap. The research aims to evaluate the effectiveness of machine learning as a technique for the prediction of defaulters in the fixed-line telecommunication sector, and to develop an effective predictive model for the purpose. The success of machine learning techniques in analysis and prediction over traditional methods prompted its use in this study. The study followed the design science research methodology. An analysis was conducted based on past transaction data. Special consideration was given to the scenario of customers with little to no transaction history. Based on the analysis, a feature list for identifying defaulters was compiled, and multiple predictive models were developed and evaluated in comparison. The resulting predictive model, which uses the Random Forest technique, shows high performance in all considered aspects. The findings of the study demonstrate that machine learning techniques can effectively predict defaulters in the fixed-line telecommunication sector, with significant implications for mitigating the risk associated.