Smart Computing and Systems Engineering - 2025 (SCSE 2025)

Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/30037

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Author: search.filters.author.Chandrasena, M.Has files: Yes

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    Bridging Linguistic Gaps: A Review of AI- Driven Speech-to-Speech Translation for Sinhala and Tamil in Sri Lanka
    (Department of Industrial Management, Faculty of Science, University of Kelaniya., 2025) Dilshani, I.; Chandrasena, M.
    Sri Lanka maintains two official languages which constitute Sinhalese and Tamil making it linguistically diverse. Almost all communication requires effective dialogue between Sinhalese-speaking and Tamil-speaking communities especially when operating through real-time speech-to-speech translation. The current version of Speech-to-Speech Translation (S2S) solutions serves a useful purpose yet faces three major limitations, including internet dependence, performance difficulties in loud environments, and unnatural Text-to-Speech (TTS) outputs. The Automatic Speech Recognition (ASR) systems from CallTran along with Android-based solutions through Google APIs and PocketSphinx, struggle with flexible operations when processing different accent varieties. Furthermore, the Machine Translation (MT) system performs poorly in achieving semantic relevance due to the scarcity of parallel corpora. The combination of ASR, MT and TTS systems produces performance delays and misinterpretation issues, which interfere with real-time functionality. This review examines current models, highlights theoretical and practical gaps, and proposes directions for future research, followed by a comparison of existing approaches. The research requires attention to three essential gaps, including bilingual dataset annotation tasks alongside offline functionality and natural voice synthesis development. We propose future research directions to establish massive bilingual datasets as well as implement noise-resistant ASR models using self-supervised approaches such as Whisper and Wave-to-Vector v2 (Wav2Vec2) and the fine-tuning of multilingual MT models like Multilingual Bidirectional and Auto-Regressive Transformer (mBART) for Low Resource Sinhala-Tamil Language translation systems. Additionally, TTS models like Tacotron, FastSpeech and Coqui TTS should be optimized for prosody and intonation.
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    Predicting Medical Drug Sales in a Specific Area for Categorical Drugs using Time Series Forecasting
    (Department of Industrial Management, Faculty of Science, University of Kelaniya., 2025) Ekanayake, S. B.; Lakmal, G.; Perera, A.; Nasmeen, M.; Vimanshani, P.; Chandrasena, M.
    Accurately forecasting pharmaceutical drug sales is a significant challenge faced by many firms, particularly in Sri Lanka, where factors such as seasonality, weather, local health crises, importation issues, currency fluctuations, and economic instability affect inventory management. These challenges often lead to frequent conditions of either shortages or overstocking of drugs, which adversely affect healthcare delivery and business profitability. This study addresses this issue through the development of a data-driven system using machine learning to predict drug sales efficiently and accurately. This work involved gathering sales data from local pharmacies, performing pre-processing steps, and implementing a time-series forecast using the SARIMA model, which works efficiently with seasonal variations in sales data. A locally hosted, user-friendly web application was developed using the Flask framework to present these predictions in a readable format for pharmacists and drug sellers. The system was also validated on an external dataset, demonstrating high accuracy in the forecasted sales, which helped improve inventory management practices. The proposed system reduces drug shortages, minimizes wastage due to expiration, and enhances supply chain efficiency, thereby improving healthcare delivery and business outcomes. This research provides evidence of the opportunity to leverage pharmaceutical sales data to identify disease trends and inform public health strategies. The model can be further improved and applied in various aspects by including additional variables. This research bridges gaps in supply chain management, improving the availability of medications and making inventory management more predictable, benefiting both public health and industry stakeholders.