Research Publications Authored by SLIIT Staff
Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/4195
This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.
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Publication Embargo English Language Trainer for Non-Native Speakers using Audio Signal Processing, Reinforcement Learning, and Deep Learning(IEEE, 2021-12-02) Jeewantha, H. C. R.; Gajasinghe, A. N; Rajapaksha, T. N; Naidabadu, N. I; Kasthurirathna, D.; Karunasena, A.Lack of basic proficiency and confidence in writing and speaking in English is one of the major social problems faced by most non-native English speakers. Although the general adult literacy rate in Sri Lanka is above average by world standards, the English literacy rate is just 22% among the Sri Lankan adult population. Many individuals face setbacks in achieving their career and academic goals due to these language barriers. In a world where English has become a compulsory requirement to pursue higher education, career development, and performing day-to-day activities, "English Buddy" is a software solution developed to enhance the English learning experience of individuals in a more personalized and innovative way. The system provides an all-in-one solution while filling major research and market gaps in existing solutions in the e-learning domain. The system consists of a personalized learning environment, automated pronunciation error detection system, automated essay evaluation process, automated descriptive answer evaluation component based on semantic similarity, and real-time course content rating system. English Buddy is implemented using state-of-the-art technologies such as Audio Signal Processing, Reinforcement Learning, Deep Learning, and NLP. The LSTM, Sentiment Analysis, and Siamese network models have gained accuracy scores of 0.93, 0.92, and 0.81 respectively. Further, the UAT results proved that the personalized recommendations and pronunciation error detection processes are accurate and reliable. This research has been able to overcome the limitations of most existing solutions that follow traditional approaches and provide better results compared to the recent studies in the e-learning research domain.Publication Embargo Sinhala to english language translator(IEEE, 2008-12-12) De Silva, D; Alahakoon, A; Udayangani, I; Kumara, V; Kolonnage, D; Perera, H; Thelijjagoda, SThis paper describes a machine translation system that is capable of translating a grammatically correct Sinhala sentence in to its corresponding English sentence. This is the first Sinhala to English machine translation system, which comes with features such as an inbuilt keyboard, an inbuilt dictionary, an integrated word processor based on Unicode fonts, a grammar tool, a Sinhalese grammar checker, an add word tool, and a debugging tool. With the expansion of the world, English has become an important language that people should learn, as the majority of the worldwide population understand and carry out their day-to-day work in English. In addressing this need, we thought of taking up the challenge of building, a Sinhala to English language translator. To build this system, we used the transfer-based machine translation approach, which is a rule-based approach. At present, the system has achieved a success rate of 75% with a corpus of 150 sentences.