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Author: search.filters.author.Ranasinghe, AHas files: Yes

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    PublicationOpen Access
    Fast Fashion’s Waste Crisis: A Systematic and Bibliometric Review of Global Evidence (2015–2025)
    (University of Nigeria Department of Mass Communication, 2026-06-01) Dayapathirana, N; Siriwardhane, D; Galdolage, S; Ranasinghe, A
    Background: The rising prominence of the fast fashion business model has generated significant concerns regarding textile industry waste and the availability of mitigation strategies. Analysing the consumption patterns driven by fast fashion is essential to determining their long-term environmental impacts. Objective: This study aims to synthesise existing research on fast fashion, textile waste, and sustainability to identify the principal areas of academic inquiry and research trends over the last decade. Methodology: The research analysed 124 articles indexed in Scopus between 2015 and 2025. Following the PRISMA protocol, a bibliometric analysis was conducted to determine publication trends, key authors, and dominant thematic clusters within the identified literature. Results: Three primary research domains were identified: 1) consumer behaviours, motivations, and psychological drivers; 2) environmental impacts of fast fashion; and 3) circular economy principles and sustainable consumption strategies, including industrial interventions. Unique Contribution: This study provides a comprehensive overview of the fast fashion and textile waste literature, filling an existing gap by establishing a wide-ranging research agenda that bridges the divide between consumer psychology and waste management. Conclusion: While sustainable fashion is an accelerating research field, a significant gap remains between theoretical research and the practical application of sustainable consumption and waste disposal models in the fast fashion sector. Key Recommendation: Future research should explore the interconnectedness of consumer purchasing habits, textile disposal behaviours, and the efficiency of existing waste management infrastructures to foster a truly circular economy.
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    Understanding AI Chatbot Adoption in Education: The Role of Perceived Usefulness, Ease of Use, and Anthropomorphic Tendencies
    (Institute of Electrical and Electronics Engineers Inc., 2025) Vidarshika, W; Dayapathirana, N; Ranasinghe, A
    This paper aims to highlight the underlying factors influencing AI-based ChatGPT usage behavior, considering the role of anthropomorphic tendency. It addresses existing gaps in AI literature, which has underexplored the anthropomorphization of nonhuman agents with human features in AI-based teaching and learning. This study extends the Technological Acceptance Model (TAM) integrating anthropomorphism tendency on usage behavior of ChatGPT of undergraduates. Empirical examination with Structural Equation Modeling (SEM) revealed that perception of ease of use and usefulness positively impact on attitudes and attitudes positively impacts on AI ChatGPT usage behavior in higher education. Furthermore, the novelty brings for the study with the anthropomorphism tendency as a moderator positively moderates perception of usefulness and ease of use on AI ChatGPT usage behavior in higher education. As the main theoretical contributions of the study this study contributes for the Technological Acceptance Model (TAM) identifying perceived ease of use and usefulness towards attitude and usage behavior and bringing anthropomorphism tendency for the model as moderator as one of lack of focused area in extant literature in AI based ChatGPT. Also, this study provides valuable insights for the designers of AI based ChatGPT in embedding the humanistic feature in enhancing the usefulness and ease of use towards their attitudes and usage behavior
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    PROBEXPERT: An Enhanced Q&A Platform for Reducing Time Spent on Learning and Finding Answers
    (IEEE, 2022-07-18) Thennakoon, K; Ekanayake, D; Marapana, T; Ranasinghe, A; Wijendra, D. R; Gamage, A
    The World Wide Web contains a wide range of material from a variety of fields. However, when concerns towards the computer science domain, information users find on the internet may not be up-to-date due to the rapid pace of change and having to spend less time on the internet for researching and debugging tasks is an added luxury. Having an expertise level while providing answers through a platform is convenient for users, yet when a user signs into a platform, the user must start from the beginning, regardless of the level of competence in the field. Moreover, not having a proper way to evaluate the existing programming knowledge is another obstacle. To address mentioned complications, researchers of this paper have introduced a new e-learning platform- ‘ProbExpert’. The platform has been constructed with machine learning and deep learning approaches such as NLP, keyword extraction, semantic information analysis, cosine similarity, and information summarization. With aforesaid technologies, ProbExpert provides systems in automated answering, optimized answer generation, structured question-based quiz evaluation together with a fully automated portfolio generation with a novel user ranking algorithm based on the bell curve.
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    Enhanced Secure Solution for PoS Architecture
    (IEEE, 2019-12-05) Samaranayake, C; Kuruppu Achchige, R. P; Shanaz, T; Ranasinghe, A; Senarathne, A. N
    Today retail businesses expect to bring the utmost in sales and payment transactions by adapting new technologies. Therefore, Advanced Point of Sales (PoS) Systems are widely used in the industry. Regardless of how efficient and secure these systems or applications work, unexpected information security risks can arise. Such risks could be a threat to their business and organization. It is important to ensure that critical information such as payment card information, handled in PoS systems is kept secure from attacks that could bring financial loss. This research provides a solution by studying the overall infrastructure of a PoS System and identifies the key events that such data would be at risk. The major concern of it was to enhance the existing security features of the system to avoid any type of malicious activity. This research consists of four main sections under security related to PoS Systems that would address the risk; Studying of malware and classifying them, detecting possible attacks and means of preventing it, a robot (BOT) to predict and generate the system status with a Data Leakage Prevention(DLP) solution for all the events occurring at a PoS. The key objective of implementing this solution was to protect the confidential data that is being used in the PoS System and to avoid threats that lead to the unavailability of the system. The implemented security features using machine learning and Deep Learning methods to the existing PoS functions produced a 99.3% of accuracy in Malware Detection and 95% of accuracy in its Classification process while the DLP Solution was able to obtain an accuracy of 84.6%. The above results retrieved fulfilled the research objectives and aided to integrate an enhanced security solution for a PoS system.