Scopus Index Publications
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This collection consists of all Scopus-indexed publications produced by SLIIT researchers. Scopus is recognized worldwide as a leading and reputable academic indexing database.
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Item Embargo Sustainability Insights: Unveiling the Impact of Business Analytics in Shaping Sustainability Practices in the Apparel Industry(2025) Gajanayake, L; Rajapaksha, D; Rukshan, T; Pathirana, S; Thelijjagoda, S; Pathirana, GThe Sri Lankan apparels industry has a strategic importance for the national economy as the country has been one of the main exports and employers. But it has sustainability issues such as high resource consumption, increased pollution, and poor labor standards. As the consumption of sustainable and environmentally responsible clothes continues to rise around the world, such concepts as business analytics (BA) present an opportunity to tackle these issues. This study investigates the effects of BA tools and techniques in enhancing sustainability in Sri Lanka apparel industry with regards to waste reduction, efficient resource management and compliance to ethical standards for sustainable driven global business. A qualitative research design was followed and conventional interviews conducted on key informants from GOTS certified apparel factories. Data were coded and analyzed thematically using MAXQDA software, with reference to the subthemes that emerged in the study, such as waste reduction and increasing efficiency and effective decision-making. It was revealed that BA solutions such as RFID, predictive modelling and dynamic dashboards offered promising improvements to sustainability performance. Techniques like 3D sampling reduced fabric consumption during the generation of prototypes, and dashboard analytics allowed constant tracking of other forms of sustainability KPIs like power use and carbon footprint. They also increased efficiency of cross-functional coordination, integrating sustainability into functions and departments. This study demonstrates how BA enables the sustenance of development within the apparel industry, based on a strategic management of economical, ecological, and social goals. The outcomes would help industry leaders and policymakers in developing improved strategies for sustainability practice to overcome existing gaps between theory and practice and for sustainable and competitive business growth in the context of a world economy moving toward sustainability.Item Embargo AI-Driven Fault-Tolerant ETL Pipelines for Enhanced Data Integration and Quality(Institute of Electrical and Electronics Engineers Inc., 2025) Wickramaarachchi, C.K; Perera, S.K; Thelijjagoda, SThe reliability and fault tolerance of ETL (Extract, Transform, Load) pipelines are essential for maintaining data integrity in corporate environments. Traditional ETL systems often depend on manual interventions to resolve data inconsistencies, leading to errors, inefficiencies, and increased operational costs. This study introduces an AI-driven framework designed to improve the fault tolerance of ETL processes by automating data cleaning, standardization, and integration tasks. Using machine learning models, the framework reduces the need for human intervention, enhances data quality, and supports scalability across various data formats. Using real-world data sets, the proposed solution demonstrates its ability to improve operational efficiency and reduce errors within corporate data pipelines. This research addresses a crucial gap in ETL automation, offering a scalable and proactive approach to robust data integration in large-scale corporate settings. The findings highlight the ability of the framework to improve fault tolerance, improve data quality, and offer organizations a competitive advantage in managing complex data ecosystems.Item Embargo WORDEX: Early Dyslexia Detection and Support(Institute of Electrical and Electronics Engineers Inc., 2025) Ganegoda, S.H; Dissanayake, O; Samarakoon, S; Jayawardana, N; Thelijjagoda, S; Gunathilake, PDyslexia is a prevalent and complex learning disability that affects approximately 5% of primary school students worldwide. It often manifests as persistent difficulties in reading, writing, spelling, and overall academic performance, which can lead to long-term educational and psychological impacts if not addressed early. To facilitate the early identification and support of dyslexic learners aged 7 to 10, this paper introduces Wordex, an innovative and adaptive educational platform. Wordex is designed to screen for multiple dyslexia subtypes and provide targeted interventions through engaging, interactive, and personalized learning activities. The platform features an integrated machine learning-based screening system that analyzes user interactions and performance metrics to assess the risk of dyslexia. Upon identification, the platform delivers tailored remedial exercises that align with national school curricula, aiming to strengthen specific cognitive and linguistic skills. Wordex is developed using a modern technology stack including Spring Boot, Flutter, Python libraries, Firebase, and MongoDB, and incorporates capabilities such as image processing, supervised learning algorithms, real-time progress tracking, and cloud-based data management. A user-centered design approach and iterative testing cycles were employed to ensure the platform is accessible, intuitive, and pedagogically effective. Wordex contributes significantly to the field of educational technology by offering a scalable, research-informed intervention tool. Future enhancements include multilingual support, broader age group coverage, and integration with classroom learning environments.Item Embargo MindBridge: Early Identification of Learning Difficulties in Children as a Supporting Tool for Teachers(Institute of Electrical and Electronics Engineers Inc., 2025) Mapa, N; Deshapriya, M; Premathilake, M; Samarakoon, S; Thelijjagoda, S; Vidanaralage, A.JLearning difficulties in children significantly impede academic success by affecting information processing, mathematical performance, and the learning of proper reading and writing. This paper proposes a Progressive Web Application (PWA) based on artificial intelligence (AI) and machine learning (ML) for identifying potential learning barriers. In contrast with standard diagnostic instruments, the proposed system is designed as a prediction tool with the potential for teachers to conduct timely and focused interventions. By automating feature extraction and reducing manual processing, the system overcomes the limitations of existing learning systems and improves early detection accuracy. Preliminary evaluations indicate that the PWA can effectively identify at-risk students and improve intervention methods and overall academic performance. This research contributes to the integration of computational methods and pedagogy, offering a scalable and low-cost solution for helping slow learners overcome their learning challenges.Item Embargo "articulearn": An Integrative, AI-Driven Speech Therapy System for Children With Speech Disorders(Institute of Electrical and Electronics Engineers Inc., 2025) Ranasinghe, K; Zoysa, S.P.D; Annasiwatta, S; Fernando, P; Thelijjagoda, S; Weerathunga, I"ArticuLearn", a personalized speech therapy system for children with speech sound disorders that integrates advanced machine learning techniques and interactive digital tools to provide targeted intervention across four key domains: phonological disorder detection, fluency disorder identification and intervention, therapy for childhood apraxia of speech, and personalized speech activity filtering for articulation disorders. By leveraging dedicated LSTM-based classifiers and feature extraction techniques such as Mel-frequency cepstral coefficients (MFCCs), this approach automatically identifies specific error types, including phoneme substitutions, omissions, and vowel mispronunciations. In addition, a hierarchical deep learning framework employing attention mechanisms and dynamic time warping is applied to quantify motor planning deficits associated with childhood apraxia of speech, while a reinforcement learning agent adapts therapy prompts based on individual performance. Data were collected from eight children per disorder category along with a normative sample of twenty typically developing children, providing a basis for personalized intervention and progress monitoring. ArticuLearn is designed to complement traditional therapy methods by offering an accessible, scalable solution that supports remote intervention and enhances clinical decision-making. Pilot evaluations suggest that the system can facilitate targeted speech exercises, improve self-monitoring, and foster adaptive learning in young users. This research underscores the potential of combining AI-driven analysis with interactive therapy to transform speech rehabilitation, particularly in resource-limited settings where access to specialized care is challenging.Item Embargo Context-Aware Behavior-Driven Pipeline Generation(Institute of Electrical and Electronics Engineers Inc., 2025) Gunathilaka, P; Senadheera, D; Perara, S; Gunawardana, C; Thelijjagoda, S; Krishara, JEnterprise networks increasingly rely on cloud platforms, remote collaboration tools, and real-time communication, placing high demands on bandwidth availability and responsiveness. Static bandwidth allocation approaches often fail to adapt to dynamic traffic conditions, leading to congestion, inefficiency, and degraded Quality of Service (QoS) for critical services such as VoIP and video conferencing. This research introduces a novel real-time bandwidth allocation system that integrates Deep Packet Inspection (DPI), supervised machine learning, and Linux traffic control (tc). Unlike prior solutions that focus only on classification or simulation, our system actively enforces bandwidth policies based on live predictions. Traffic is captured and analyzed in the WAN, while adaptive policies are deployed in the LAN. A web dashboard offers real-time traffic and bandwidth visibility. The proposed system addresses realworld enterprise challenges by enabling intelligent, responsive bandwidth management without requiring costly infrastructure changes, achieving measurable improvements in latency, throughput, and application-level prioritizationPublication Open Access Personal well-being index as a measure of quality of life of diverse groups of people with visual impairment and blindness(Springer Science and Business Media B.V., 2024-04) Jayathilaka, R; Dunuwila, V; Attale, D; De Seram, H; Sudusinghe, D; Abeyrathna, I; Suraweera, T; Thelijjagoda, SToday, the world adopts various assessment tools and indices to measure quality of life (QoL) of different persons. The Personal Well-being Index (PWI) is a popular and validated tool used by developed countries to assess the QoL of their citizens. The PWI consists of seven major domains that define people’s QoL. Thus, the main purpose of this study is to explore the application of PWI in measuring the QoL of the visually impaired and blind (VI and B) persons in Sri Lanka, and to identify how QoL varies with their demographic characteristics. Primary data revealed among 64 VI&B, 34 blind and 30 visually impaired people from Hambanthota, was analysed based on vision level, age, gender, marital status, and the level of education. Results indicated that visually impaired (VI) respondents had a higher PWI value than that of the blind. Accordingly, the age group of 40–59 contributes to a higher PWI value than that of others; while the results signify that the PWI values basically depend on the levels of education the participants received. It is significant that the blind and the partially sighted people are concerned about their future security to a greater extent compared to the other domains in the PWI. Also, QoL was perceived to deteriorate with age. Thus, it is evident that efforts to improve QoL of people with visual disabilities requires priority to secure a fruitful and secure future for them.