Browsing by Author "Hewapathirana, Isuru Udayangani"

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    Advancing tourism demand forecasting in Sri Lanka: evaluating the performance of machine learning models and the impact of social media data integration
    (Emerald Publishing Limited, 2023) Hewapathirana, Isuru Udayangani
    Purpose – This study explores the pioneering approach of utilising machine learning (ML) models and integrating social media data for predicting tourist arrivals in Sri Lanka. Design/methodology/approach – Two sets of experiments are performed in this research. First, the predictive accuracy of three ML models, support vector regression (SVR), random forest (RF) and artificial neural network (ANN), is compared against the seasonal autoregressive integrated moving average (SARIMA) model using historical tourist arrivals as features. Subsequently, the impact of incorporating social media data from TripAdvisor and Google Trends as additional features is investigated. Findings – The findings reveal that the ML models generally outperform the SARIMA model, particularly from 2019 to 2021, when several unexpected events occurred in Sri Lanka. When integrating social media data, the RF model performs significantly better during most years, whereas the SVR model does not exhibit significant improvement. Although adding social media data to the ANN model does not yield superior forecasts, it exhibits proficiency in capturing data trends. Practical implications – The findings offer substantial implications for the industry’s growth and resilience, allowing stakeholders to make accurate data-driven decisions to navigate the unpredictable dynamics of Sri Lanka’s tourism sector. Originality/value – This study presents the first exploration of ML models and the integration of social media data for forecasting Sri Lankan tourist arrivals, contributing to the advancement of research in this domain.
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    Advancing tourism demand forecasting in Sri Lanka: evaluating the performance of machine learning models and the impact of social media data integration
    (Emerald Publishing Limited, 2023) Hewapathirana, Isuru Udayangani
    Purpose – This study explores the pioneering approach of utilising machine learning (ML) models and integrating social media data for predicting tourist arrivals in Sri Lanka. Design/methodology/approach – Two sets of experiments are performed in this research. First, the predictive accuracy of three ML models, support vector regression (SVR), random forest (RF) and artificial neural network (ANN), is compared against the seasonal autoregressive integrated moving average (SARIMA) model using historical tourist arrivals as features. Subsequently, the impact of incorporating social media data from TripAdvisor and Google Trends as additional features is investigated. Findings – The findings reveal that the ML models generally outperform the SARIMA model, particularly from 2019 to 2021, when several unexpected events occurred in Sri Lanka. When integrating social media data, the RF model performs significantly better during most years, whereas the SVR model does not exhibit significant improvement. Although adding social media data to the ANNmodel does not yield superior forecasts, it exhibits proficiency in capturing data trends. Practical implications – The findings offer substantial implications for the industry’s growth and resilience, allowing stakeholders to make accurate data-driven decisions to navigate the unpredictable dynamics of Sri Lanka’s tourism sector. Originality/value – This study presents the first exploration of ML models and the integration of social media data for forecasting Sri Lankan tourist arrivals, contributing to the advancement of research in this domain.
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    Navigating the age of AI influence: A systematic literature review of trust, engagement, efficacy and ethical concerns of virtual influencers in social media
    (Hewapathirana IU, Perera N. (2024). Navigating the age of AI influence: A systematic literature review of trust, engagement, efficacy and ethical concerns of virtual influencers in social media. Journal of Infrastructure, Policy and Development. 8(8): 6352. https://doi.org/10.24294/jipd.v8i8.6352, 2024) Hewapathirana, Isuru Udayangani; Perera, Nipuni
    This systematic literature review (SLR) delves into the realm of Artificial Intelligence (AI)-powered virtual influencers (VIs) in social media, examining trust factors, engagement strategies, VI efficacy compared to human influencers, ethical considerations, and future trends. Analyzing 60 academic articles from 2012 to 2024, drawn from reputable databases, the study applies specific inclusion and exclusion criteria. Both automated and manual searches ensure a comprehensive review. Findings reveal a surge in VI research post- 2012, primarily in journals, with quantitative methods prevailing. Geographically, research focuses on Europe, Asia Pacific, and North America, indicating gaps in representation from other regions. Key themes highlight trust and engagement’s critical role in VI marketing, navigating the balance between consistency and authenticity. Challenges persist regarding artificiality and accountability, managed through brand alignment and transparent communication. VIs offers advantages, including control and cost efficiencies, yet grapple with authenticity issues, addressed through human-like features. Ethically, VI emergence demands stringent guidelines and industry cooperation to safeguard consumer well-being. Looking ahead, VIs promises transformative storytelling, necessitating vigilance in ethical considerations. This study advocates for continued scholarly inquiry and industry reflection to navigate VI marketing evolution responsibly, shaping the future influencer marketing landscape.
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    A Review of Recent Trends in Sri Lankan Social Media Analytics Research
    (2023) Hewapathirana, Isuru Udayangani; Sandaruwani, M. D.
    Due to industry demands and massive applications, the social media landscape is rapidly expanding. However, in Sri Lanka, analyzing social media data is still considered a young research topic. This article examines the present status of social media analytics research in Sri Lanka, highlighting selected technologies and applications and discussing their proven and future benefits. The primary goal of this research is to provide information regarding social media analytics usage in Sri Lanka and to identify shortcomings in this area. We select 45 publications published between 2013 and 2022 from the most used web-based databases, including Google Scholar, IEEE Xplore, ScienceDirect, Springer, and ResearchGate. To identify eligible papers for thorough analysis, multi-phase searches and selections are accomplished. The study also includes extensive discussions on social media platforms and the technology, tools, and techniques used in analytics. The review discovered several methodologies and tools that were utilized with social media data. Descriptive analysis, regression analysis, and text analysis were the most commonly used analysis methods, while Facebook, Twitter, YouTube, Instagram, and Viber were the most popular social media networks. Current social media analytics research were noticed in a variety of domains, including marketing, education, politics, health, social, and business.
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    A Systematic Investigation on the Effectiveness of the Tabbert Model for Credit Card Fraud Detection
    (2022) Hewapathirana, Isuru Udayangani; Kekayan, Nanthakumar; Diyasena, Deshanjali
    As a result of rapid digitisation, online transactions using credit cards have become popular. With this, fraudulent activities have also increased considerably. Although many supervised and unsupervised machine learning techniques were proposed in past research for identifying fraudulent transactions, they do not fully utilize the tabular and hierarchical structure present in transaction datasets. Recently, the TabBERT neural network model was proposed to calculate row-wise embeddings that capture both inter and intra dependencies between transactions in tabular time series data. In this research, we present a systematic experimental framework to assess the effectiveness of applying the embeddings calculated using the TabBERT model for credit card fraud detection. We employ the calculated row embeddings for fraud detection using three unsupervised machine learning algorithms and two supervised machine learning algorithms. We perform our experiments on a synthetic dataset that has been generated using the TabGPT model. Overall, TabBERT-based embeddings increase the performance of the supervised learning models with the extreme gradient boosting model achieving a precision of 99% and an F1 score of 98%, and the multilayer neural network model achieving a precision of 97% and an F1 score of 95%. For unsupervised learning, the use of TabBERT embeddings increases the recall rate of K-means clustering algorithm by 0.19%.