Research Publications Authored by SLIIT Staff

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Sinhala Part of Speech Tagger using Deep Learning Techniques
(IEEE, 2022-12-21) Sathsarani, M.W.A.R.; Thalawaththa, T.P.A.B.; Galappaththi, N.K.; Danthanarayana, J.N.; Gamage, A
Natural Language Processing (NLP) is a sub-field of Artificial Intelligence (AI) that consists of a collection of computational methods motivated by theory for the automated classification and reflection of human languages. The foundation for many sophisticated applications of NLP, including named entity recognition, sentiment analysis, machine translation, in-formation retrieval, and information processing, is laid by Part of Speech (POS) tagging, which is part of the lexical layer of NLP systems. In contrast to English, French, German, and other languages from the same geographical region, the development of high-accuracy, stable POS taggers for the Sinhala language is still in its early stages. Hence, Sinhala is identified as a low-resource language. The main objective of this research is to create a POS tagger for the Sinhala language to solve this issue. An innovative and novel strategy that has never been used with the Sinhala language has been designed. This approach has been suggested specifically to evaluate the possibility of enhancing the accuracy compared to other methodologies. So, deep learning algorithms have been applied in this study, which has a significant impact on improving tagger performance. First, highly accurate individual classifiers for primary POS tags were implemented, and then they were combined into one composite model. As expected, all individual classifiers and the final composite model have achieved a higher accuracy level. Thus, it demonstrates that the proposed solution using deep learning algorithms outperformed other methods, such as rule-based and stochastic, in terms of accuracy.
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Deep Learning Approach for Designing and Development of Risk Level Indicator for Patients with Lung Diseases
(IEEE, 2022-02-23) Chathurika, K. B. A. B; Gamage, A
"Lung disease" as a medical term, discusses as several disorders that affects both lungs. There are different types of lung disease like Asthma, lungs infections like Influenza, Pneumonia, Tuberculosis, and numerous other types of breathing problems including Lung cancers. These lung diseases can be the main reason for failure in breathing. Due to COVID19 pandemic, Pneumonia and COVID19 were highlighted mostly as fatal diseases if not detected on time. Newly identified COVID19 diseases has caused many deaths and confirmed detections reported worldwide, followed with a greatest risk to community wellbeing, especially for patients with lung diseases. Process of developing a clinically accepted vaccine or specific therapeutic drug for this disease are not finalized, which will contribute to the expansion of actual prevention action plans. Thus, methods to detect lung illness accurately and efficiently is important. Proposed solution will easily and precisely detect the risk level of patients with these two lung diseases Pneumonia and COVID19 using a mobile application with chest radiography (Chest X-rays), which is considered as a cheap, easy to access and speedy manner. Proposed solution will identify, classify and evaluate the risk level of the patient suffering with the use of Image Processing, Machine Learning techniques and Convolutional Neural Networks. So, anybody who use the proposed solution may have the ability to have a precious decision about own medical condition accurately, quickly with low cost. Proposed solution can calculate severity level of a patient with more than 97% accuracy with chest radiography analysis together with patient’s current symptoms and breath holding time evaluation.

Research Publications Authored by SLIIT Staff

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